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http://ri.uaemex.mx/handle20.500.11799/94750| Title: | Computing the number of groups for color image segmentation using competitive neural networks and fuzzy c-means | Authors: | Jair Cervantes Canales Farid García Lamont ASDRUBAL LOPEZ CHAU JOSE SERGIO RUIZ CASTILLA |
Keywords: | Color characterization;Color spaces;Competitive neural networks;info:eu-repo/classification/cti/7 | Publisher: | Springer | Description: | Se calcula la cantidad de grupos en que los vectores de color son agrupados usando fuzzy c-means Fuzzy C-means (FCM) is one of the most often techniques employed for color image segmentation; the drawback with this technique is the number of clusters the data, pixels’ colors, is grouped must be defined a priori. In this paper we present an approach to compute the number of clusters automatically. A competitive neural network (CNN) and a self-organizing map (SOM) are trained with chromaticity samples of different colors; the neural networks process each pixel of the image to segment, where the activation occurrences of each neuron are collected in a histogram. The number of clusters is set by computing the number of the most activated neurons. The number of clusters is adjusted by comparing the similitude of colors. We show successful segmentation results obtained using images of the Berkeley segmentation database by training only one time the CNN and SOM, using only chromaticity data. |
URI: | http://ri.uaemex.mx/handle20.500.11799/94750 | Other Identifiers: | http://hdl.handle.net/20.500.11799/94750 | Rights: | info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0 |
| Appears in Collections: | Producción |
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